Stars
☕ A tool to generate requirements.txt for Python project, and more than that. (IT IS NOT A PACKAGE MANAGEMENT TOOL)
Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding b…
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Training, optimization and deployment of Object Detection model with dinov2 backbone for efficient inference on NVIDIA Jetson
OpenMTP - Advanced Android File Transfer Application for macOS
An open source implementation of CLIP.
This repository contains methods for the automatic extraction of urban street furniture from labeled PointClouds.
The official implementation of "CityDreamer: Compositional Generative Model of Unbounded 3D Cities". (CVPR 2024)
[CVPR2024 Hightlight] No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
Tracking instruction-tuned LLM openness. Paper: Liesenfeld, Andreas, Alianda Lopez, and Mark Dingemanse. 2023. “Opening up ChatGPT: Tracking Openness, Transparency, and Accountability in Instructio…
A network-based python package to understand and model urban complexity
Code and dataset of paper "TOWARDS ACCURATE INSTANCE SEGMENTATION IN LARGE-SCALE LIDAR POINT CLOUDS"
Python package for segmenting LiDAR data using Segment-Anything Model (SAM) from Meta AI.
A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
Automated mapping of pedestrian networks from aerial imagery tiles
GlobalMatch: Registration of forest terrestrial point clouds by global matching of relative stem positions [ISPRS 2023]
(TPAMI2023) Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey
Code release
An easy-to-use Python library for processing and manipulating 3D point clouds and meshes.
This repository contains the implementation of Kernel Point Convolution (KPConv) to decompose the pole-like objects to their constituent part (part segmentation of the pole-like objects).
Techniques for deep learning with satellite & aerial imagery
Python Package for Airborne RGB machine learning
Tree detection from aerial imagery in Python
Repository for automatic classification and labeling of Urban PointClouds using data fusion and region growing techniques.
[CVPR2021] SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
Laspy is a pythonic interface for reading/modifying/creating .LAS LIDAR files matching specification 1.0-1.4.